package FlinkTest.day06;

import com.atguigu.datastream.bean.WaterSensor;
import org.apache.flink.api.common.eventtime.*;
import org.apache.flink.api.common.functions.MapFunction;
import org.apache.flink.api.java.tuple.Tuple;
import org.apache.flink.streaming.api.datastream.DataStreamSource;
import org.apache.flink.streaming.api.datastream.KeyedStream;
import org.apache.flink.streaming.api.datastream.SingleOutputStreamOperator;
import org.apache.flink.streaming.api.datastream.WindowedStream;
import org.apache.flink.streaming.api.environment.StreamExecutionEnvironment;
import org.apache.flink.streaming.api.functions.windowing.ProcessWindowFunction;
import org.apache.flink.streaming.api.windowing.assigners.TumblingEventTimeWindows;
import org.apache.flink.streaming.api.windowing.time.Time;
import org.apache.flink.streaming.api.windowing.windows.TimeWindow;
import org.apache.flink.util.Collector;

import java.time.Duration;

import static org.apache.flink.util.Preconditions.checkArgument;
import static org.apache.flink.util.Preconditions.checkNotNull;

/**
 * ClassName: Flink01_Flink_Customer_WaterMark
 * Package: FlinkTest.day06
 * Description:
 *           有序流创建水位线
 * @Author ChenJun
 * @Create 2023/4/13 11:11
 * @Version 1.0
 */
public class Flink01_Flink_Customer_WaterMark {
    public static void main(String[] args) throws Exception {

        //1. 创建流的执行环境
        StreamExecutionEnvironment env = StreamExecutionEnvironment.getExecutionEnvironment();
        env.setParallelism(1);

        //2. 从端口读取数据
        DataStreamSource<String> streamSource = env.socketTextStream("localhost", 9999);

        //3. 将数据转换为JavaBean
        SingleOutputStreamOperator<WaterSensor> map = streamSource.map(new MapFunction<String, WaterSensor>() {
            @Override
            public WaterSensor map(String value) throws Exception {
                String[] split = value.split(",");

                return new WaterSensor(split[0], Long.parseLong(split[1]), Integer.parseInt(split[2]));
            }
        });

        //TODO 4.指定WaterMark以及事件时间戳   使用有序流中的WaterMark
        SingleOutputStreamOperator<WaterSensor> waterSensorSingleOutputStreamOperator =
                map.assignTimestampsAndWatermarks(WatermarkStrategy.<WaterSensor>forGenerator(new WatermarkGeneratorSupplier<WaterSensor>() {
                    @Override
                    public WatermarkGenerator<WaterSensor> createWatermarkGenerator(Context context) {
                        return new MyWatermarkGenerator(Duration.ofSeconds(3));
                    }
                } ).withTimestampAssigner(new SerializableTimestampAssigner<WaterSensor>() {
                    @Override
                    public long extractTimestamp(WaterSensor element, long recordTimestamp) {
                        return element.getTs() * 1000;
                    }
                }));

        //5. 将相同id的数据聚合到一块
        KeyedStream<WaterSensor, Tuple> keyedStream = waterSensorSingleOutputStreamOperator.keyBy("id");

        //TODO 6.开启一个基于事件时间的滚动窗口，窗口大小为5S
        WindowedStream<WaterSensor, Tuple, TimeWindow> window = keyedStream.window(TumblingEventTimeWindows.of(Time.seconds(5)));


        SingleOutputStreamOperator<String> process = window.process(new ProcessWindowFunction<WaterSensor, String, Tuple, TimeWindow>() {
            @Override
            public void process(Tuple tuple, ProcessWindowFunction<WaterSensor, String, Tuple, TimeWindow>.Context context, Iterable<WaterSensor> elements, Collector<String> out) throws Exception {
                String msg =
                        "窗口: [" + context.window().getStart() / 1000 + "," + context.window().getEnd() / 1000 + ") 一共有 "
                                + elements.spliterator().estimateSize() + "条数据 ";
                out.collect(msg);
            }
        });

        SingleOutputStreamOperator<WaterSensor> result = window.sum("vc");

        process.print();
        result.print();

        env.execute();


    }

    //  TODO 自定义一个类实现WatermarkGenerator这个接口
    public static  class MyWatermarkGenerator implements WatermarkGenerator<WaterSensor>{

        //当前最大的时时间挫
        private long maxTimestamp;

        // 乱序程度
        private  long outOfOrdernessMillis;

        public MyWatermarkGenerator(Duration maxOutOfOrderness) {
            //获取传入的乱序成都
            this.outOfOrdernessMillis = maxOutOfOrderness.toMillis();

            //起始watermark的值为long的最小值
            this.maxTimestamp = Long.MIN_VALUE + outOfOrdernessMillis + 1;
        }

        /**
         * 每条数据调用一次   可以生成Watermark
         * @param event
         * @param eventTimestamp
         * @param output
         */
        @Override
        public void onEvent(WaterSensor event, long eventTimestamp, WatermarkOutput output) {
            System.out.println("onEvent:生成watermark："+(maxTimestamp - outOfOrdernessMillis - 1));
            maxTimestamp = Math.max(maxTimestamp, eventTimestamp);
            output.emitWatermark(new Watermark(maxTimestamp - outOfOrdernessMillis - 1));
        }

        /**
         * 每隔200ms 调用一次 可以以生成watermark
         * @param output
         */
        @Override
        public void onPeriodicEmit(WatermarkOutput output) {
//            System.out.println("生成watermark："+(maxTimestamp - outOfOrdernessMillis - 1));
//            output.emitWatermark(new Watermark(maxTimestamp - outOfOrdernessMillis - 1));

        }
    }
}
